Assessment of Adaptive Coordinated Control of Overcurrent Relay Using Novel Multi-Trial Vector-Based Differential Evolution Algorithm

References

• M. Singh, B. K. Panigrahi and A. R. Abhyankar, “Optimal coordination of directional over-current relays using Teaching Learning-Based Optimization (TLBO) algorithm,” Int. J. Electr. Power Energy Syst, vol. 50, pp. 33–41, 2013. DOI: 10.1016/j.ijepes.2013.02.011.
   Web of Science ®Google Scholar
• M. Singh, B. K. Panigrahi, A. R. Abhyankar and S. Das, “Optimal coordination of directional over-current relays using informative differential evolution algorithm,” J. Comput. Sci., vol. 5, no. 2, pp. 269–276, 2014. DOI: 10.1016/j.jocs.2013.05.010.
   Web of Science ®Google Scholar
• A. M. Ibrahim, W. El-Khattam, M. ElMesallamy and H. A. Talaat, “Adaptive protection coordination scheme for distribution network with distributed generation using ABC,” J. Electr. Syst. Inform. Technol., vol. 3, no. 2, pp. 320–332, 2016. DOI: 10.1016/j.jesit.2015.11.012.
   Google Scholar
• A. T. Peiris, J. Jayasinghe and U. Rathnayake, “Forecasting Wind Power Generation Using Artificial Neural Network:”Pawan Danawi”—A Case Study from Sri Lanka,” J. Elect. Comput. Eng., vol. 2021, pp. 1–10, 2021. DOI: 10.1155/2021/5577547.
   Web of Science ®Google Scholar
• V. D. Thanh, V. Van Phuong, M. Q. Duong and Q. Tran, “Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms,” Energies, vol. 13, no. 11, pp. 2873, 2020. DOI: 10.3390/en13112873.
   Web of Science ®Google Scholar
• G. Nazaré, R. Castro and L. R. A. Gabriel Filho, “Wind power forecast using neural networks: tuning with optimization techniques and error analysis,” Wind Energy, vol. 23, no. 3, pp. 810–824, 2020. DOI: 10.1002/we.2460.
   Web of Science ®Google Scholar
• N. Shahraki, H. Mohammad, S. Taghian, S. Mirjalili and H. Faris, “MTDE: an effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems,” Appl. Soft Comput., vol. 97, pp. 106761, 2020. DOI: 10.1016/j.asoc.2020.106761.
   Web of Science ®Google Scholar
• H. Lotfi, T. L. Mohammadbaghernaghibi and S. Hesari, “Short and Mid-Term Wind Power Forecasting with ANN-PSO,” Int. J. Multidiscip. Curr. Res., vol. 2, pp. 1026–1031, 2014. http://ijmcr.com/short-and-mid-term-wind-power-forecasting-with-ann-pso/.
   Google Scholar
• S. Singh, T. S. Bhatti and D. P. Kothari, “Wind Power Estimation Using Artificial Neural Network,” ASCE J. Energy Eng., vol. 133, no. 1, pp. 46–52, 2007. DOI: 10.1061/(ASCE)0733-9402(2007)133:1(46).
   Web of Science ®Google Scholar
• S. Mishra, R. Prusty and P. Kumar Hota, “Analysis of Levenberg- Marquardt and Scaled Conjugate gradient training algorithms for artificial neural network based LS and MMSE estimated channel equalizers,” Proc. 2015 Inter. Conf. Man Mach. Interfacing (MAMI), pp. 1–7, December, Bhubaneswar, Odisha, 2015. DOI: 10.1109/MAMI.2015.7456617.
   Google Scholar
• H. Liu, “On the Levenberg-Marquardt training method for feed-forward neural networks,” Proc. Inter. Conf. natural Comput., Vol. 1, Yantai, China, August, pp. 456–460, 2010. DOI: 10.1109/ICNC.2010.5583151.
   Google Scholar
• A. Tealab, H. Hefny and A. Badr, “Forecasting of nonlinear time series using ANN,” Fut. Comput. Inform. J., vol. 2, no. 1, pp. 39–47, 2017. DOI: 10.1016/j.fcij.2017.05.001.
   Google Scholar
• R. Kalpana, et al., “Internet of Things (IOT) Based Machine Learning Techniques for Wind Energy Harvesting,” Electric Power Comp. Syst., pp. 1–17, 2023. DOI: 10.1080/15325008.2023.2293952.
   Web of Science ®Google Scholar
• V. Senthil Kumar, et al., “Optimizing solar power plant efficiency through advanced analytical framework and comparative analysis,” IFS, vol. 46, no. 2, pp. 4995–5011, 2024. DOI: 10.3233/JIFS-235578.
   Google Scholar
• M. N. Ali, M. Amer and M. Elsisi, “Reliable IoT paradigm with ensemble machine learning for faults diagnosis of power transformers considering adversarial attacks,” IEEE Trans. Instrum. Meas., vol. 72, pp. 1–13, 2023. DOI: 10.1109/TIM.2023.3300444.
   PubMed Web of Science ®Google Scholar
• M. Elsisi, C. L. Su and M. N. Ali, “Design of reliable IoT systems with deep learning to support resilient demand side management in smart grids against adversarial attacks,” IEEE Trans. Ind. Applicat., vol. 60, no. 2, pp. 2095–2106, 2024. DOI: 10.1109/TIA.2023.3297089.
   Google Scholar
• W. S. Cheng, G. Y. Chen, X. Y. Shih, M. Elsisi, M. H. Tsai and H. J. Dai, “Vickers hardness value test via multi-task learning convolutional neural networks and image augmentation,” APPl. Sci., vol. 12, no. 21, pp. 10820, 2022. DOI: 10.3390/app122110820.
   Google Scholar
• M. Elsisi, M. Amer, A. Dababat and C.-L. Su, “A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation,” Energy, vol. 281, pp. 128256, 2023. DOI: 10.1016/j.energy.2023.128256.
   Web of Science ®Google Scholar
• G. Sakkarvarthi, G. W. Sathianesan, V. S. Murugan, A. J. Reddy, P. Jayagopal and M. Elsisi, “Detection and classification of tomato crop disease using convolutional neural network,” Electronics, vol. 11, no. 21, pp. 3618, 2022. DOI: 10.3390/electronics11213618.
   Web of Science ®Google Scholar
• M. Tran, et al., “Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach,” Measurement, vol. 207, pp. 112398, 2023. DOI: 10.1016/j.measurement.2022.112398.
   Web of Science ®Google Scholar
• M. H. Hussain, S. R. A. Rahim and I. Musirin, “Optimal overcurrent relay coordination: a review,” Proc. Eng., vol. 53, pp. 332–336, 2013. DOI: 10.1016/j.proeng.2013.02.043.
   Google Scholar
• M. R. Tur, A. Shobole, M. Baysal and W. A. D. I. Mohammed, “Protection Coordination Practice in Electrical Substation Part-1 Overcurrent and Earth Fault Protection-Case Study of Siddik Kardesler Substation (SKS),” Gazi Uni. J. Sci., vol. 30, no. 4, pp. 180–198, 2017. https://www.researchgate.net/publication/320057590_Protection_Coordination_Practice_in_Electrical_Substation_Part-1_Overcurrent_and_Earth_Fault_Protection.
   Google Scholar
• Xiangyu, Zheng, Rong, Jia, Linling, Gong, Xiping, Ma, Jian, Dang, Aisikaer, “Relay protection system of transmission line based on AI,” Plos One, vol. 16, no. 4, pp. e0246403, 2021. DOI: 10.1371/journal.pone.0246403.
   PubMed Web of Science ®Google Scholar
• P. Mariaraja, T. M. T. Manigandan and S. T. S. Thiruvenkadam, “Advanced development and Design of multiphase radial distribution system workstation for performing load flow analysis under normal and abnormal conditions,” J. Electr. Eng., vol. 19, no. 1, pp. 8–8, 2019. https://www.semanticscholar.org/paper/ADVANCED-DEVELOPMENT-AND-DESIGN-OF-MULTIPHASE-FOR Mariaraja/36f2d215ee0a74911507cf650565ad4c6da65123.
   Google Scholar